Abstract
Big data is characterized in many circles in terms of the three V's - volume, velocity and variety. Although most of us can sense palpable opportunities presented by big data there are overwhelming challenges, at many levels, turning such data into actionable information or building entities that efficiently work together based on it. This chapter discusses ways to potentially reduce the volume and velocity aspects of certain kinds of data (with sparsity and structure), while acquiring itself. Such reduction can alleviate the challenges to some extent at all levels, especially during the storage, retrieval, communication, and analysis phases. In this chapter we will conduct a non-technical survey, bringing together ideas from some recent and current developments. We focus primarily on Compressive Sensing and sparse Fast Fourier Transform or Sparse Fourier Transform. Almost all natural signals or data streams are known to have some level of sparsity and structure that are key for these efficiencies to take place.
Original language | English |
---|---|
Title of host publication | Managing Big Data Integration in the Public Sector |
Publisher | IGI Global |
Pages | 137-147 |
Number of pages | 11 |
ISBN (Electronic) | 9781466696501 |
ISBN (Print) | 1466696494, 9781466696495 |
DOIs | |
State | Published - 1 Jan 2015 |